‘Product’ and ‘factor’ revaluations in Seton-Leontief type Models: illustrations from Indian Institutional sectors
The exclusion of “demand” or “utility” considerations from the value formation has been a source of discomfort with linear models i.e. SraffaLeontief/Von-Newmann type models. These models accurately capture various ‘costs of production’ versions of classical political economy.
Howevr in cooperation of use value based determinants in value formation has remained elusive for these models. Seton’s [Cost, use, & value]
book attempts to redeem the impasse by posing “utility and factor cost based valuations” as the two entities to be simultaneously (therefore
mutually complementing and consistent) discovered. His approach re-symthesises Maria Augustinovics suggestion with conventional labor/capital
based valuations.
Consider an economy with three sectors. The output and deliveries made by them being reckoned in their own units or currencies i.e. Coal-Rupees,
power rupees, etc.
Coal
80 crs
20 prs
6 krs
39 lrs
150 crs
(X)
Coal
Power
Service
(W) Capital
Labor
(Z) Output
Power
10 crs
25 prs
18 krs
39 lrs
90 prs
Service
30 crs
25 prs
10 srs
6 krs
27 lrs
85 srs
Final outputs (Z)
30 crs
20 prs
75 srs
𝐶𝑟𝑠 = 𝑐𝑜𝑎𝑙 𝑟𝑢𝑝𝑒𝑒𝑠, 𝑝𝑟𝑠 = 𝑝𝑜𝑤𝑒𝑟 𝑟𝑢𝑝𝑒𝑒𝑠 𝑎𝑛𝑑 𝑠𝑟𝑠 = 𝑠𝑒𝑟𝑣𝑖𝑐𝑒 𝑟𝑢𝑝𝑒𝑒𝑠
Or 𝑋 + 𝑊 ′ = 𝑍′ and 𝑋 + 𝑌 = 𝑍′. In terms of technical coefficients 𝐴 = 𝑋𝑍 −1 and 𝐴𝑍 −1 + 𝑊 ′ = 𝑍′.
The ‘classical labor values’, where only labor is the cause of value (or for neo-classicals ‘only scarce resource’), can be used to describe
production processes of an economy as given by following simultaneous equations:
80
crs
prs
lrs
crs
+ 20
+ 39
= 150
lrs
lrs
lrs
lrs
10
crs
prs
lrs
prs
+ 25
+ 27
= 90
lrs
lrs
lrs
lrs
10
prs
lrs
srs
+ 17
= 85
lrs
lrs
lrs
Solution of which is
1 𝑐𝑟𝑠 = 0.7 𝑙𝑟𝑠; 1 𝑝𝑟𝑠 = 0.52 𝑙𝑟𝑠 𝑎𝑛𝑑 1 𝑠𝑟𝑠 = 0.68 𝑙𝑟𝑠
To Use Leontief notation this is
𝑐𝐿′ = 𝑏𝐿′ (𝐼 − 𝐴)−1
Where,
𝑏𝐿′ is the row vector of homogeneous labor
𝑐𝐿′ is the row vector of Marxian labor values
Let Seton’s ‘𝐴˥ ' denote (𝐼 − 𝐴)−1 (read as ‘A Lin’)
Instead of labor if capital was taken as the only factor causing value, then similarly, following equation would yield the capital values:
𝑐𝐾′ = 𝑏𝐾′ 𝐴˥
𝑜𝑟 𝑐𝐾′ = 𝑏𝐾′ (𝐼 − 𝐴)−1
For the above quoted numerical example capital values work out as:
1 𝑐𝑟𝑠 = 0.17 𝑘𝑟𝑠, 1 𝑝𝑟𝑠 = 0.30 𝑘𝑟𝑠 𝑎𝑛𝑑 1 𝑠𝑟𝑠 = 0.68 𝑘𝑟𝑠
Thus, following matrix of valuations provides capital and labor based valuations as rows, respectively:
Capital based 0.17 0.30 0.25
Labor based 0.70 0.52 0.68
Generlising in terms of matrix notation, 𝑟 𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟 − 𝑏𝑎𝑠𝑒𝑑 𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛 is given by:
𝑐𝑟′ = 𝑏𝑟′ 𝐴˥
Where,
𝑏𝑟′ is 𝑟 𝑡ℎ type of factor-input per unit of total output
And
𝑐𝑟′ denotes factor-input 𝑟 embodied in both direct and indirect manner
Any non-‘capitalist’ and non-‘Marxist’ would like to get rid of these labor mono-maniac values and combine both labor and capital embodied.
However, combining involves the problem of attaching weights. If labor and capital are weighed at par (i.e. equal weights assigned to them, 𝑓1 =
𝑓2), composite valuation is obtained:
0.17𝑓1 + 0.70𝑓2
[ 0.30𝑓1 + 0.52𝑓2 ]
0.25𝑓1 + 0.68𝑓2
0.415
= [ 0.41 ]
0.465
If labor is weighted twice heavily as capital, the full composite factor cost would be [1.57 1.73 1.61].
1
Composite costs can be further charged with a mark-up factor = (∅ − 1). Full –factor costs thus obtained are:
1
1
1
(0.17𝑓1 + 0.70𝑓2 ); (0.30𝑓1 + 0.52𝑓2 ); (0.25𝑓1 + 0.68𝑓2 )
∅
∅
∅
Yet there is no unique principle available to ascertain values 𝑓1 , 𝑓2 and ∅.
Now consider there is some arbitrary notion of “use-value” (not necessarily same as utility in Walras-Jevons sense!) and that only a particular
commodity, say ‘coal’, results or provides the economy ‘utility’ or ‘satisfaction’. In that case, other goods would have a contribution in use-value
so far as they facilitate the production of ‘coal’. Such a system would imply all transactions to be valued in terms o ‘inal consumable coal’.
Following equations describe the system:
𝑉𝑘𝑖 = 𝑚𝑘 + (𝑞𝑖1 𝑉𝑘1 + 𝑞𝑖2 𝑉𝑘2 + ⋯ + 𝑞𝑖𝑛 𝑉𝑘𝑛 )
Where,
of
Elements of vector 𝑞𝑟 are the output quotas of sector i allocated to i-consuming sectors i.e. they are row-transaction coefficients instead
being
the
usual
column-transaction
coefficients.
Thus, 𝑄 = 𝑍 −1 𝑋, ℎ𝑜𝑤𝑒𝑣𝑒𝑟, 𝐴 = 𝑋𝑍 −1
⇒ 𝑄 = 𝑍 −1 . 𝐴. 𝑍
𝑎𝑛𝑑 𝑄 ˥ = (𝐼 − 𝑄)−1 = 𝑍 −1 . 𝐴˥ . 𝑍
Final output quota per unit of output is then given by
𝑌𝑖 ⁄𝑍𝑖 = 𝑀𝑖 𝑖. 𝑒. 𝑌𝑖 . 𝑍 −1 = 𝑀
0.45
Coal-based valuations, therefore, are [0.14]. Choice of ‘power’ and ‘service’ as the ‘norm’ or basis of use-valuations instead of coal would yield
0.00
0.05
0.51
in similar vein [0.32] and [0.54], respectively. Thus, there may exist ‘n’ commodity-based norms if there exist ‘n’ commodities. But again the
0.00
1.00
choice of norm remains arbitrary. Even I it is believed that each commodity has a use-value still there is no information on the weights (describing
substitutability in use-levels) that are needed to be attached to each commodity, say (𝑃1 , 𝑃2 ⋯ , 𝑃𝑛 ) .
Choice of any commodity to be used as ‘use-norm’ requires factor-use (i.e. costs) to be revalued in term of same norm. For example, if ‘power
norm’ is selected then the inputs (labor and capital) will be valued as:
(6 × 0.05) + (18 × 0.32) + (6 × 0) = 6.06
6.06 = 𝑇𝑜𝑡𝑎𝑙 𝑙𝑎𝑏𝑜𝑟 𝑖𝑛𝑝𝑢𝑡 (= 30) × 0.202
Similarly,
(39𝐿 × 0.05) + (27𝐿 × 0.32) + (17𝐿 × 0) = 10.59
Thus, capital will be revalued by the factor 0.13. since this is valuation of factor used it is called norming of factor or ‘factor-norm’.
Collecting factor norms in a matrix gives:
Capital norm
0.174
0.20
0.63
Labor norm
0.256
‘coal’ based
0.13
‘power’ based
0.62
‘service’ based
If 𝑃1 , 𝑃2 ⋯ , 𝑃𝑛 are taken weights for use-based norms, then
0.17𝑃1 + 0.20𝑃2 + 0.63𝑃3
0.26𝑃1 + 0.13𝑃2 + 0.62𝑃3
will be the factor norm matrix.
Note that in ‘cost-approach’ choice was to be made between 𝑓1 , 𝑓2 , however, in ‘use-based’ valuation choice is to be made from
𝑃1 , 𝑃2 , 𝑃3 . However Marshallian synthesis demands a mutual consistency between these two choice problems. 𝑓1 , 𝑓2 𝑎𝑛𝑑 ф on one
hand and 𝑃1 , 𝑃2 , 𝑃3 on the other, must be chosen such that
𝑃1 =
0.17𝑓1 + 0.70𝑓2
∅
, 𝑃2 =
0.30𝑓1 + 0.52𝑓2
∅
𝑎𝑛𝑑 𝑃3 =
0.25𝑓1 + 0.68𝑓2
∅
And
𝑓1 = 0.17𝑃1 + 0.20𝑃2 + 0.63𝑃3
𝑓2 = 0.26𝑃1 + 0.13𝑃2 + 0.62𝑃3
These criterions cannot be fulfilled simultaneously by an arbitrary choice. This can be easily understood in terms of matrix
formulation. Norm-matrix is given by:
′
𝑁=𝑚
̂ (𝑄 ˥ ) 𝑅 ′ = 𝑌𝐶 ′ 𝑊 −1
This is further used to calculate cost-norm matrix (𝐹 = 𝐶𝑁) and norm-cost matrix (𝑃 = 𝑁𝐶). Matrices calculated for the quoted
example are given below:
80
𝑋 = [20
0
10
25
0
30
25]
10
30
𝑌̂ = [ 0
0
0
20
0
0
0]
85
150
𝑍̂ = [ 0
0
0
90
0
0
0]
85
𝑚
̂ = 𝑌̂𝑍̂ −1 = [
0.2000
0
0
6
𝑊=[
39
𝑤
̂ =[
30
0
0.5333
𝑄 = 𝑍 −1 𝑋 = [0.2222
0
0
0.2222
0
18
27
0
0 ]
0.8824
6
]
17
0
]
83
0.0667 0.2000
0.2778 0.2778]
0
0.1176
0.5333 0.1111 0.3529
𝐴 = 𝑋𝑍 −1 = [0.1333 0.2778 0.2941]
0.000
0.000 0.1176
2.2414 0.3448 1.0115
𝐴˥ = (𝐼 − 𝐴)−1 = [0.4138 1.4483 0.6483]
0.00
0.000 1.1333
0.040 0.2000 0.0706
𝐵=[
]
0.2600 0.3000 0.2000
0.1724 0.3034 0.2501
𝐶 = 𝐵𝐴˥ = [
]
0.7069 0.5241 0.6841
0.1724
′
𝑁=𝑚
̂ (𝑄 ˥ ) 𝑅 ′ = 𝑌𝐶 ′ 𝑊 −1 = [0.2023
0.6253
0.2103
𝑃 = 𝑁𝐶 = [0.1242
0.5448
0.1862
0.1276
0.5138
0.2475
𝐹 = 𝐶𝑁 = [
0.6557
0.2555
0.1263]
0.6182
0.2179
0.1370]
0.5793
0.2370
]
0.6697
Leaving behind the arbitrariness and stated in the language of matrix algebra, left hand eigen vectors of 𝐹 and 𝑃corresponding to the
largest positive root are to be found. For the numerical example, solutions for eigen vector of 𝐹 ′ and 𝑃′ are:
0.5761
0.7792
𝑝′ = [0.5419] , 𝑓 ′ = [
] 𝑎𝑛𝑑 ф = 0.9058
0.7823
0.6119
The above have to be scaled up to maintain the GNP level same i.e. (∑𝑌 = 125).
(0.5761 × 30) + (0.5419 × 20) + (0.6119 × 75) = 74.0135
Hence,
125⁄74.0135 = 1.6889
and
0.9730
0.5761
𝑝∗ = 1.6889 [0.5419] = [0.9152]
1.0334
0.6119
0.9991
]. if all sectors were to have 𝑝𝑖 = 1, that can be
1.0031
when ∑𝑌 = 𝐺𝑁𝑃 is taken as a weight, thus, ∑𝑝𝑖 . 𝑌 = ∑𝑌. When the weights to be assigned are unknown, implicitly same weight is
given
to
each
commodity
when
dealing
with
actual
data
in
current
prices.
Therefore,
Similarly, scaling 𝑓 ′ yields an adjustment factor equal to 1.2822, thus 𝑓 ∗ = [
𝑝′ ∑(𝑌 ′ ⁄𝑌0 ) = 1
Thus 1⁄𝑝∗ < 1 implies under-valuation in actual prices and 1⁄𝑝∗ > 1 implies over-valuation in current prices. In the example,
therefore, ‘coal’ and ‘power’ sectors are over-valued and ‘service’ sector is under-valued.
Similarly ‘capital input’ needs to be scaled down by (0.9991) and labor input to be scaled up by (0.0031). Revaluated in these ideal
prices original table of inter-industry transaction and factor matrix (W) will appear as follows:
77.838
𝑋 ∗ = [18.3043
0.000
9.7298
22.8804
0.000
29.1893
22.8804]
11.3344
5.9945
39.1195
17.9836
27.0828
5.9945
]
17.0521
𝑊∗ = [
Since the 𝑝∗ and 𝑓 ∗ are eigen vectors i.e. they have a ratio like definition, they are amenable to comparison over time and across
countries. Ф provides the basis for mark-up factor from the eigen factor-norm equation.
Calculating Ф amounts to discovering a ‘uniform rate of surplus’ or a ‘maximum rate of value-added tax’ which is equal across the
sectors. Thus, procedure guarantees equal rate of surplus after revaluating the factor-rentals. It revaluates the capital input to arrive at
uniform rate of ‘VAT’ or ‘surplus’. In this sense it modifies the definition of transformation problem to solve it as it neither involves
remolding of labor-values into prices nor it requires two accounting systems (i.e. price and value). Moreover, it resembles Marshallian
synthesis where utility-based value of factor and factor-based price of commodity are uniquely related.
II
An important advantage with this formulation is the freedom in choosing number of factor-inputs and their ‘observed’ values.
Using1⁄𝑝∗ < 1 𝑜𝑟 > 1, will then suggest the over-valuation and under-valuation. In other words, ‘distortions’ would then be revealed
in terms of over and under valuations. Thus, ‘current data’ is examined from its own ‘intrinsic information. The heterogeneous labor
market could be even segregated in other than traditional distinctions of ‘land’, ‘labor’ and ‘capital’. For example different types of
‘land’ or ‘skilled and unskilled labor’ or ‘public and private capital’, etc..
The process described above is illustrated in this paper by splitting the value-added (taken as a factor input valued in terms of its
returns) into ‘organized and unorganized’ incomes for the Indian Economy. 14 sector classification (Table 1) of National Accounts
Statistics, C.S.O., is used to do the same. Relevant structural ratios are summarized in Table 2. Treatment of imports has led to three
models which are discussed as follows.
Model 1, where imports are taken as complementary, therefore Total output and final use vectors taken from Input-Output tables as it
is, while total value of output of sectors was calculated. The results arrived at by applying this model are given in Table 4(a).
Model 2, where imports follow total supply approach i.e. total output ‘Z’ and total final use ‘Y’ are increased by the imports, though
artificially, compensated for by including the imports as special factor ‘input’ in each sector. The relevant tables are given in Table
4(b).
Model 3, where value of imports of each sector are segregated in ratio of (O)/(U) value added shares and added to respective value
added. Results of this model are given in Table 4(c).
Application of the outlined procedure in section I of the paper yields eigen prices and eigen-factor norms, given in Table 4(a), 4(b) and
4(c).
Results reveal the following:

In all the three models following sectors have been under-valued:
1
2
Agriculture
Forestry and logging
3
11
12
13
14
Fishing
Trade, hotels and rest.
Banking and insurance
Other services
Public administration & defense
Note that the above sectors are a part of either primary or tertiary sector’s activities.

In all the three models following sectors were over-valued :
5
6
7
9

Manufacturing
Construction
Electricity, gas and water supply
Transport by other means and storage
Following sectors’ status of under or over valuation changed as treatment of imports are changed:
4
Commodity↓
Mining
1973-74
1983-84
2003-04
8 Railway transport services 1973-74
1983-84
2003-04
10 Communication
1973-74
1983-84
2003-04
Model 1
over-valued
over-valued
over-valued
over-valued
under-valued
under-valued
under-valued
under-valued
under-valued
Model 2
over-valued
under-valued
over-valued
over-valued
under-valued
under-valued
over-valued
under-valued
under-valued
Model 3
over-valued
under-valued
under-valued
over-valued
under-valued
under-valued
over-valued
under-valued
under-valued

Changes in the extent of under or over valuation of each commodity have changed over the years. direction of these changes is
mentioned below:
Direction of over/under valuation over years
1
2
3
4
5
6
7
8
9
10
11
12
13
14

Commodity ↓
Agriculture
Forestry and logging
Fishing
Mining
Manufacturing
Construction
Electricity, gas and water
supply
Railway transport services
Transport by other means
and storage
Communication
Trade, hotels and rest.
Banking and insurance
Other services
Public administration &
defence
Model 1
Under-valuation increased
Under-valuation increased
Under-valuation decreased
Over- valuation decreased
Over- valuation increased
Over- valuation increased
Model 2
Under-valuation increased
Under-valuation increased
Under-valuation decreased
Over- valuation decreased
Over- valuation decreased
Remained Stable
Model 3
Under-valuation increased
Over- valuation decreased
Under-valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Over- valuation decreased
Under-valuation increased
Under-valuation decreased
Remained Stable
Under-valuation increased
Over- valuation decreased
Under-valuation decreased
Under-valuation increased
Under-valuation increased
Over- valuation decreased
Under-valuation decreased
Over- valuation decreased
Over- valuation decreased
Remained Stable
Remained Stable
Over- valuation decreased
One should notice that 𝑄 ˥ bears close relationship to 𝐴˥ . The eigen prices are evidently influenced by final output to gross
output ratio as well as final output composition. Higher the 𝑌𝑖 ⁄𝑍𝑖 ratio, there is higher likelihood of 𝑝∗ > 1 and vice-versa.
Such is the case with many primary sectors. As Kravis had noted that service sector often shows higher shares from value
added by sector and lower share in final output. This is referred to as the ‘splintering services’ by Bhagwati. Thus, it may be
the case that lower yield based 𝑚
̂ = 𝑌𝑖 ⁄𝑍𝑖 used in price calculations causes the valuation to produce ‘under-pricing’.


When viewed from factor-input side the ‘organized factors’ have their cost over-valued by 2.03 per cent for the year 1973-74
as per model 1. Similarly imports are ‘over-valued’. Similarly, imports are over-valued by 8.8 per cent. Thus import-users and
‘organized segment’ earn quasi-rent and cause deprivation of unorganized sector. Dandekar (1986) argues income transfer of
roughly 2.5 to 3 per cent from organized incomes to achieve poverty eradication which looms large in unorganized sector. Our
estimates are only marginally lower (=2.03%). If over-valued imports are predominantly consumed in the organized segments
then implicit transfer appears to be in agreement with Dandekar’s.
Similar scanning of factor-rentals by ownership (e.g. public and private) f factors would also be feasible.
III
Data of Indian economy has been gathered from Input-Output Transactions Tables of 1973 to 2004 and Factor Income (2008). Both
publications are of Central Statistical Organization (C.S.O.)
Bibliography
Augustinovics, Maria. Input Output Analysis, Proceedings of the fourth International Conference on Input-Output techniues, Geneva (1968),
Methods of international and intertemporal comparison of structure. North - Holland Publishing Company, 1970.
Seton, Francis. Cost, Use, & Value. Nuffield College, Oxford: Oxford University Press, 1984.
Table 1: Aggregation scheme (60 to 14)
14 sector classification
1 Agriculture
2 Forestry and logging
3 Fishing
4 Mining
5 Manufacturing
6 Construction
7 Electricity, gas and water supply
8 Railway transport services
9 Transport by other means and storage
10 Communication
11 Trade, hotels and rest.
12 Banking and insurance
13 Other services
14 Public administration & defence
1
6
7
8
12 to 44
45
46
48
49
51
52
54
56
60
2
3
4
9
10
11
58
59
47
50
53
55
57
5
Table 2 : Some structural ratios of Indian economy (%)
Commodities →
1
Composition of Final Output (Z)
1973-74
Z (-M)†
37.67
Z(+M)‡
38.26
1983-84
Z (-M)
24.87
Z(+M)
24.00
2003-04
Z (-M)
13.97
Z(+M)
12.94
2
3
4
5
6
7
8
9
10
11
12
13
14
0.59
0.61
1.25
1.20
0.49
0.50
0.47
0.48
0.44
0.42
0.62
0.57
0.66
0.06
1.86
2.82
1.44
3.02
28.16
27.17
31.90
33.43
34.01
37.23
6.55
6.77
7.23
6.88
8.61
7.92
1.19
1.23
2.49
2.37
3.04
2.79
1.25
1.30
1.43
1.36
1.08
0.99
3.61
3.64
4.16
4.17
7.15
6.63
0.40
0.42
0.44
0.42
1.12
1.03
9.11
9.42
9.96
9.51
10.87
10.00
1.42
1.47
2.04
1.96
4.11
3.87
6.46
6.67
9.32
8.97
10.44
9.72
2.44
2.53
2.61
2.48
3.05
2.80
Shares of Inputs
1973-74
(O)
(U)
1983-84
(O)
(U)
2003-04
(O)
(U)
1.25
98.75
7.40
92.60
2.86
97.14
100
0
58.09
41.91
12.46
87.54
0
100
0.47
99.53
0.03
99.97
89.94
10.06
94.13
5.87
91.10
8.90
62.23
37.77
57.66
42.34
72.83
27.17
50.94
49.06
55.84
44.16
37.53
62.47
100
0
93.13
6.87
97.02
2.98
100
0
100
0
100
0
35.86
64.14
39.01
60.99
20.92
79.08
100
0
100
0
64.21
35.79
8.12
91.88
6.74
93.26
19.43
80.57
98.54
1.46
96.79
3.21
90.45
9.55
39.76
60.24
27.97
72.03
39.96
60.04
100
0
100
0
100
0
Final-use variable
Import Composition
1973-74
1983-84
2003-04
20.31
6.47
1.34
0.09
0.11
0.73
0.10
0.05
0.02
18.71
22.02
20.86
57.83
64.14
73.64
0
0
0
0
0
0
0
0
0
2.96
4.30
0.70
0
0.17
0.01
0
0.43
0.14
0
0.42
1.08
0
1.88
1.48
0
0
0
Z (-M)† and Z(+M)‡ denote Final outputs excusive and inclusive of imports, respectively
Source: Input-output transactions tables of 1973 to 2004, C.S.O., Min. of P & PI
1980-81 - 1999-2000, Statement 2.1 and 3.1, National Accounts Statistics, Factor Incomes (BASE YEAR 1999-2000), C.S.O., Min. of S & PI/2008
Table 3(a) : Model 1
1973-74
Commodity ↓
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
Agriculture
Forestry and logging
Fishing
Mining
Manufacturing
Construction
Electricity, gas and water supply
Railway transport services
Transport by other means and storage
Communication
Trade, hotels and rest.
Banking and insurance
Other services
Public administration & defence
Factors↓
(O)
(U)
Eigen cost ratio
p
1.043683
1.026894
1.045746
0.98334
0.950709
0.961631
0.23021
0.934849
0.905501
1.00519
1.032459
1.017573
1.005461
1.035766
P*
0.958145
0.97381
0.956256
1.016942
1.051847
1.0399
4.343866
1.069691
1.104361
0.994837
0.968562
0.982731
0.994569
0.965469
f
f*
0.980067
1.020338
1.00722
0.992832
0.958366748
1983-84
p
1.057555
1.025745
1.039891
0.994473
0.973993
0.946096
0.951349
1.143637
0.926044
1.017176
1.00983
1.024337
0.955473
1.036865
P*
0.945577
0.974901
0.961639
1.005557
1.026701
1.056975
1.051139
0.874403
1.079862
0.983114
0.990266
0.976242
1.046602
0.964446
f
f*
0.990642
1.009446
1.005278
0.99475
0.96448391
2003-04
p
1.127722
1.037068
1.0375
0.989627
0.948626
0.941781
1.058051
1.027172
0.942431
1.037437
0.999857
1.011551
1.018906
1.0194
P*
0.886744
0.964257
0.963856
1.010482
1.054157
1.061818
0.945134
0.973547
1.061085
0.963914
1.000143
0.988581
0.981445
0.98097
f
f*
0.981326
1.019029
1.014043
0.986151
0.96629714
Table 3(b) : Model 2
1973-74
Commodity ↓
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
Agriculture
Forestry and logging
Fishing
Mining
Manufacturing
Construction
Electricity, gas and water supply
Railway transport services
Transport by other means and storage
Communication
Trade, hotels and rest.
Banking and insurance
Other services
Public administration & defence
Factors↓
(O)
(U)
M
Eigen cost ratio
p
1.04663
1.015217
1.047907
0.97522
0.944762
0.957153
0.228259
0.926374
0.900212
0.994034
1.032652
1.006217
1.001577
1.023577
f
1983-84
P*
0.955447
0.985011
0.954283
1.02541
1.058468
1.044765
4.380983
1.079478
1.110849
1.006002
0.96838
0.993821
0.998425
0.976966
f*
0.965684
1.035536
1.006287
0.993752
0.918594
1.088621
0.95650435
p
1.055002
1.024127
1.03727
1.006254
0.980666
0.947777
0.953171
1.145662
0.932024
1.016678
1.008563
1.02343
0.955075
1.035423
f
2003-04
P*
0.947865
0.976442
0.964069
0.993785
1.019715
1.0551
1.04913
0.872858
1.072933
0.983596
0.991509
0.977106
1.047038
0.965788
f*
0.993086
1.006963
1.006073
0.993963
0.985203
1.01502
0.967337364
p
1.126356
1.03232
1.037337
0.997739
0.962239
0.947022
1.063455
1.032903
0.947418
1.039977
1.000116
1.013374
1.019527
1.021244
f
P*
0.887819
0.968692
0.964006
1.002266
1.039243
1.055942
0.940331
0.968145
1.0555
0.96156
0.999884
0.986802
0.980847
0.979198
f*
0.988667
1.011463
1.017985
0.982332
0.969564
1.031392
0.970912192
Table 3(c) : Model 3
1973-74
Commodity ↓
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
Agriculture
Forestry and logging
Fishing
Mining
Manufacturing
Construction
Electricity, gas and water supply
Railway transport services
Transport by other means and storage
Communication
Trade, hotels and rest.
Banking and insurance
Other services
Public administration & defence
Factors↓
(O)
(U)
Eigen cost ratio
p
1.055416
0.98666
1.059036
0.377828
0.903186
0.931214
0.179078
0.886119
0.887542
0.965981
1.037267
0.979088
0.991501
0.99551
f
1983-84
P*
0.947494
1.013521
0.944255
2.64671
1.107192
1.073867
5.584169
1.128517
1.126707
1.035217
0.964072
1.021359
1.008571
1.00451
f*
0.938955
1.065014
1.020325
0.98008
0.95026787
p
1.054423
1.023143
1.036442
1.008566
0.981858
0.947687
0.952954
1.145379
0.932546
1.015829
1.007872
1.022466
0.954529
1.034181
f
2003-04
P*
0.948386
0.97738
0.964839
0.991507
1.018477
1.055201
1.049368
0.873074
1.072333
0.984418
0.992189
0.978028
1.047637
0.966949
f*
0.991896
1.008171
1.005011
0.995014
0.9673247
p
1.123656
1.034165
1.034592
1.00662
0.966635
0.946698
1.063146
1.031642
0.947193
1.037078
0.997153
1.010166
1.016738
1.016786
f
P*
0.889952
0.966964
0.966565
0.993424
1.034516
1.056303
0.940605
0.969328
1.055751
0.964247
1.002855
0.989936
0.983538
0.983491
f*
0.984351
1.015898
1.014263
0.985937
0.970823172